{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis\n",
    "\n",
    "This notebook will be useful on small data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weibull version: 0.0.13\n"
     ]
    }
   ],
   "source": [
    "import weibull\n",
    "%matplotlib inline\n",
    "\n",
    "print('weibull version:', weibull.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the failure times array and the analysis object.  When the analysis is initialized with data, it creates values for the shape parameter, $\\beta$, and the scale parameter, $\\eta$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beta: 1.09\n",
      "eta: 9855.3\n"
     ]
    }
   ],
   "source": [
    "failure_times = [\n",
    "    10197.8, 3349.0, 15318.6, 710.7, 20683.2, \n",
    "    6976.5, 2590.7, 11351.7, 10177.0, 3738.4\n",
    "]\n",
    "\n",
    "suspensions = [False, False, False, False, True, \n",
    "               False, False, False, False, False]\n",
    "\n",
    "analysis = weibull.Analysis(failure_times, suspensions, unit='hour')\n",
    "analysis.fit()\n",
    "\n",
    "print(f'beta: {analysis.beta:.02f}\\neta: {analysis.eta:.1f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check out the probability plot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files (x86)\\python36-32\\lib\\site-packages\\matplotlib\\figure.py:2022: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zu+tdYCXOe6+pBTinMSYHf2nQ/crMhOUfH8vR1PJmuG8pW+peZoOTCSkvC3W3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\nUk0YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14f41eb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis.probplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks pretty decent in this case.  For completeness, we should look at the fit tests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fit method          linear regression\n",
       "p_value                   1.81369e-06\n",
       "r_squared                    0.967554\n",
       "confidence                        0.9\n",
       "beta lower limit             0.685677\n",
       "beta nominal                  1.08978\n",
       "beta upper limit              1.73205\n",
       "eta lower limit                5853.8\n",
       "eta nominal                    9855.3\n",
       "eta upper limit               16592.1\n",
       "mean life                     9539.16\n",
       "median life                    7040.6\n",
       "b10 life                      1249.87\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis.stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# B-Life\n",
    "\n",
    "Lets take a look at how easy it is to calculate a B-life.  The B10 life and B50 life are already calculated in `analysis.stats` for convenience b/c so many customers are interested in these values.  What if you needed a B2 life?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "274.59436250145313"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis.b(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting\n",
    "\n",
    "The standard suite of plots are included..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files (x86)\\python36-32\\lib\\site-packages\\matplotlib\\figure.py:2022: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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k7FxpX2V8LfeWUMsrD84+pgF9gFTgSxHpoqoHfhSIyEhgJEDEWe34asEC4iJq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\nUEEQBEEuCQcVBEEQ5JJwUEEQBEEu+R+ZzQ47BX0e7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x151470b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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luGxctaSMh7ef4FhzFzMLs+Oy33jqWz13+bQ8bNaxL7yolFKpFMtQ8k4R6Yje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\nwMRl0etcUZqc0pzFIswuzJ6QE3NHY36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6qZTJtUUMTTgubXW9amzvQoa/EPFjhk9MPpD2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\nwckYY0zb8X9clVVZfsAI6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15082ab0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bOa26gq+cMjWnG8SzVdDvoTghGOUH7ONusof9tabRjrburJvldlNTB7e8uIam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\nqXL3oo8oCvn57LHZM8N90O9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Z1tLFvFnulpomjspjdFHI1WsYY8xIlJXBKRKNEYkO3I/8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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x151e0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bOa26gq+cMjWnG8SzVdDvoTghGOUH7ONusof9tabRjrburJvldlNTB7e8uIam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\nqXL3oo8oCvn57LHZM8N90O9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Z1tLFvFnulpomjspjdFHI1WsYY8xIlJXBKRKNEYkO3I/8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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15162a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VpEOBhySdHBErdqscMQuYBTB8+PAYM2ZMG+GWh+rqahxrx3Os2XCs2XCsiVIm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\nJygzMytLTlBmZlaW/i/eyoUrgBE8sAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x152094b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analysis.pdf()\n",
    "analysis.sf()\n",
    "analysis.hazard()\n",
    "analysis.cdf()\n",
    "analysis.fr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I realize that there is some overlap in the plots - such as `cdf` and `hazard` - but having all of the functions defined makes it easier for the maximum number of user."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
